Preprints
https://doi.org/10.5194/nhess-2021-113
https://doi.org/10.5194/nhess-2021-113

  22 Apr 2021

22 Apr 2021

Review status: this preprint is currently under review for the journal NHESS.

Integrating macroseismic intensity distributions by a probabilistic approach: an application in Italy

Andrea Antonucci1,4, Andrea Rovida1, Vera D'Amico2, and Dario Albarello3 Andrea Antonucci et al.
  • 1Istituto Nazionale di Geofisica e Vulcanologia, Milano, 20133, Italy
  • 2Istituto Nazionale di Geofisica e Vulcanologia, Pisa, 56125, Italy
  • 3Department of Physics, Earth and Environmental Sciences, University of Siena, Siena, 53100, Italy
  • 4Department of Earth Sciences, University of Pisa, Pisa, 56126, Italy

Abstract. The geographic distribution of earthquake effects quantified in terms of macroseismic intensities, the so-called macroseismic field, provides basic information for several scopes including source characterization of pre-instrumental earthquakes and risk analysis. Macroseismic fields of past earthquakes as inferred from historical documentation may present spatial gaps, due to the incompleteness of the available information. We present a probabilistic approach aimed at integrating incomplete intensity distributions by considering the Bayesian combination of estimates provided by Intensity Prediction Equations (IPEs) and data documented at nearby localities, accounting for the relevant uncertainties and the discrete and ordinal nature of intensity data. The performance of the proposed methodology is tested at 28 Italian localities with long and rich seismic histories, and for two well-known strong earthquakes (i.e., 1980 Southern Italy and 2009 Central Italy events). A possible application of the approach is also illustrated relative to a sixteenth century earthquake in Northern Apennines.

Andrea Antonucci et al.

Status: open (until 03 Jun 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Andrea Antonucci et al.

Andrea Antonucci et al.

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Short summary
We present a probabilistic approach aimed at integrating incomplete intensity distributions by considering the combination of estimates provided by Intensity Prediction Equations (IPEs) and data documented at nearby localities, accounting for the relevant uncertainties and the discrete and ordinal nature of intensity data.
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